The synergistic health impacts of exposure to multiple stressors in Tulare County, California

Tulare County is located in the Central Valley region of California (CA). Its population is exposed to stressors that include high levels of air, water, and soil pollution, socioeconomic strain, and poor access to walkable areas and healthy foods. As a result, this population suffers from a high disease burden compared to other CA counties. We hypothesize that environmental and socioeconomic stressors interact in complex ways to raise the burden of disease in the Tulare population beyond additive impacts. We used CalEnviroScreen to select Tulare County as the subject of the study and characterized the geographical interaction of stressors. The CalEnviroScreen indicators provided the basis for population-weighted average calculations to determine the most critical environmental and socioeconomic stressors in Tulare County. We also analyzed and interpreted walkability and dietary access through open-source data. In addition, we compared disease-based mortality in Tulare County to CA state averages. Our evaluation reveals that the population living within the census tracts of Tulare County is exposed to environmental stressors at significantly higher levels relative to many other Californian census tracts, specifically for fine particulate matter, ozone, and drinking water quality. Relatively high exposures to socioeconomic stressors can compound resulting health impacts. We use dose–response curves and stressor mapping to characterize how multiple stressors may augment a population’s vulnerability and effective doses from exposure to multiple stressors. According to the Centers for Disease Control and Prevention Data, the differences in mortality rates between Tulare and CA were not significant. However, notable differences in mortality between CA and Tulare occur for Alzheimer’s disease, diseases of the circulatory system, influenza, and pneumonia, which were all statistically higher in Tulare County, and for diabetes, endocrine, nutritional and metabolic diseases, and neoplasms, which were statistically lower Tulare. Previous health-impact studies have linked individual environmental stressors to their respective measures of disease. However, many communities continue to be exposed daily to numerous stressors that individually are within regulatory limits but could significantly magnify risk due to the synergistic effects. Dose–response curves tailored to population vulnerability provide a basis for quantifying the synergistic risks of multiple stressors on specific measures of disease.


Background
The effects of chemical, physical, and biological stressors on humans and other living systems have been studied extensively by exposure scientists.The National Academies report on exposure science in the 21st century describes exposure science as 'instrumental' in 'forecasting, preventing, and mitigating' exposures that have impacted the most 'vulnerable and susceptible' populations [1].The National Academies proposed the definition of a stressor as 'any physical, chemical, or biological entity that can induce an adverse response.'The World Health Organization International Programme on Chemical Safety (IPCS) takes a broader view, defining a stressor as 'any entity, stimulus, or condition that can modulate normal functions of the organism or induce an adverse response (e.g.agent, lack of food, drought) [2].In developing our list of stressors, we utilized the IPCS approach as our working definition.Moreover, in addition to chemical, physical, and biological agents, health impacts have also been linked to social, economic, and psychological stressors.These stressors can include poor air quality (high concentrations of ozone), pesticides, unemployment, etc.Recent research in exposure science has investigated the synergistic effect of multiple stressors and their 'heightened' effects on human and ecological health [3].Räsänen et al systematically reviewed current climate change literature to examine how multiple processes affecting human vulnerability have been studied [4].Their review identified many studies with multiple interacting stressors 'whose interlinkages need to be carefully analyzed and targeted by policies, which integrate adaptation to climate change and other stressors.' Menzie et al have proposed a phased approach for evaluating the effects of physical, biological, chemical, and psychosocial stressors that may act in combination [5].Their approach distinguishes between applications that begin with effects of concern (effects-based) or with specific stressors (stressor based) and then suggest appropriate tools and steps.
Much of this review is driven by the demand to gain a deeper insight into health disparities in disadvantaged communities that not only suffer higher levels of air, water, and soil pollution but also from socioeconomic stress and poor access to health care and healthy food.
Although the study of physico-chemical and psycho-social stress has been disparate due to the challenge of finding quantifiable, comparable metrics, the use of observed telomere length holds the potential to be a unifying metric to characterize the compounded effects of socioeconomic and environmental stressors.The telomere length has recently emerged as a breakthrough bioaging marker in population studies investigating chronic social stress and various environmental stressors [6,7].Specifically, the telomere are ribonucleoprotein complexes that cap the end of chromosomes and 'reflect both mitotic history and biochemical trauma to the genome' [8].
This paper provides a case study exploring the synergistic effects of stressors on the health of a specific human population in California (CA).We used CalEnviroScreen, a community-impacts mapping tool, to identify communities with the highest synergistic stressors and 'most vulnerable' populations [9].Based on CalEnviroScreen, we selected Tulare County, CA, as a case study.In contrast to the approach of Menzie et al-an effects-based or stressor-based analysis-we took a hybrid approach to consider data on the effects and stressors to develop a profile of how multiple factors converge to impact the disease burden in Tulare County [5].
Environmental stressors in Tulare County are particularly high relative to other CA counties.According to a report by the American Lung Association, Tulare County has the 'third-worst air quality' in the United States (US) [10].Additionally, the residents of Tulare County rely on municipally treated groundwater or private wells that contain contaminants at concentrations close to or exceeding the US Environmental Protection Agency (EPA) National Primary Drinking Water Regulations maximum concentration limits (MCLs) and National Secondary Drinking Water Standards [11].A considerable volume of scientific literature has shown that environmental and non-environmental stressors individually influence the development and exacerbation of diseases.For example, a study by Fan et al investigated fine particulate matter (PM 2.5 ) stressors and found that 'asthma Emergency Department (ED) visits increased at higher PM 2.5 concentrations' [12].
To examine the disease burden from multiple environmental and socioeconomic stressors, we assume that residents of Tulare County are only exposed to stressors within county boundaries.We also assume that a significant proportion of the population primarily consumes drinking water from in-county municipal or private well sources.The timeframe for this study is primarily from 2015 to 2021.

Method
To select the most essential stressors and measures of disease that characterize Tulare County, we considered CalEnviroScreen's indicators as a starting point.CalEnviroScreen categorizes its datasets into 'exposure indicators,' 'environmental effect indicators,' 'sensitive population indicators,' and 'socioeconomic factor indicators.' From this broad list of indicators, we developed our list of stressors based on consideration of (1) which indicators had high scores overall for the Tulare County population and (2) additional data on disease burden from other sources.In contrast to the scoring scheme of CalEnviroScreen, our interest is in developing quantitative measures of stressor exposures at the county level and, where possible, at the census tract scale to allow consideration of interactions.We consider CalEnviroScreen's 'exposure indicators' as environmental stressors.The 'exposure indicators' we included are ozone, PM 2.5 , drinking water contaminants, and the use of certain high-hazard, high-volatility pesticides.However, we excluded toxic releases from facilities, traffic impact, and children's lead risk from housing from our study because they likely do not meaningfully impact the disease burden in Tulare County.We also excluded 'environmental effect indicators' from our study, opting to use indicators of human health over environmental health because 'environmental effect indicators' are not stressors and do not amplify the disease burden.These excluded indicators are toxic cleanup sites, groundwater threats from leaking underground storage sites and cleanups, hazardous waste facilities and generators, impaired water bodies, and solid waste sites and facilities.
In addition, we included all of the 'sensitive population indicators' that CalEnviroScreen uses to indicate the relative health of a population.These health indicators include asthma, cardiovascular disease, and low birth-weight infants.In addition to CalEnviroScreen's health indicators, we added data provided by the Centers for Disease Control and Prevention on the leading causes of death in Tulare County.Furthermore, we included several 'socioeconomic factor indicators' as secondary stressors for this study.These are impactful non-environmental stressors that may contribute to the disease burden.These indicators include poverty, linguistic isolation, and unemployment.Additionally, we explored data on walkability and dietary access as indicators for non-environmental stressors because poor dietary access and limited walkability increase the population's disease vulnerability.Previous literature has shown strong associations between walkability, diet, and health outcomes [13,14].Other important stressors, such as educational attainment, were relevant to this study but excluded because of their inadequate evidence of impact on the selected measures of disease.Table 1 summarizes the sources of information we used to characterize the potential exposures and health consequences of each of the stressors considered.
For each census tract in Tulare County, CalEnviroScreen provides a percentile rank for exposure indicators, environmental effect indicators, sensitive population indicators, and socioeconomic factors indicators.To gain insight into Tulare County overall, we calculated each indicator's population-weighted average indicator value to explore how the overall county ranks relative to all CA census tracts.The percentile reflects the rank of each tract compared to all other CA census tracts based on the magnitude of the indicator.We calculated the population-weighted average indicators using a spreadsheet to sum the product of the population and the respective stressor rank from each Tulare County census tract.We then divided the sum by the total population of Tulare County to determine the population-weighted average indicator.

Results
This section summarizes the magnitude and range of quantitative measures for each of the stressors.We use this information to suggest where environmental and socioeconomic stressors can amplify the health outcomes of existing disease burdens.For the environmental effect indicators-ozone, PM 2.5 , drinking water, lead, and pesticides-the Tulare County equivalent percentile ranks are 89, 96, 80, 53, and 86 with higher numbers indicating higher stress.This suggests that Tulare County has a high potential disease burden from environmental stressors, mainly ozone, PM 2.5 , drinking water, and pesticides.However, Tulare also ranks high in other stressor categories that can amplify this high potential disease burden.For example, its percentile ranks relative to all CA census tracts for linguistic isolation, poverty, and unemployment are 83, 84, and 81, respectively, with higher numbers indicating higher stress.It is important to note that not all environmental indicators included in CalEnviroScreen utilize a threshold method.For example, particulate-matter-concentration percentiles are calculated with 'annual mean concentration,' as a ranking variable with no threshold while water quality indicators relied on a complex integration of average contaminant weighted sum and exceedance of the MCLs.Furthermore, these percentile scores are only relative to other CA census tracts.In the sections below, we review the quantitative ranges for each stressor individually and then explore interactions-particularly for the amplification of environmental effects stressors by non-environmental stressors.In the supporting information, we provide detailed indicator maps, walkability index, and stress plots generated by CalEnviroScreen and other sources.Additionally, we include some spatial analysis for each stressor.Additionally, we include some spatial analysis and possible explanations for each stressor.

Environmental stressors
3.1.1.PM 2.5 PM 2.5 , airborne particles less than 2.5 µm in diameter [15] has been found to increase the risk of circulatory and lower respiratory diseases, particularly for children and the elderly and cardiovascular disease in adults [16].PM 2.5 has also been linked to infant mortality and low birth weights [17].PM 2.5 varies locally by location, weather, and season [15].In many areas of Tulare County, the yearly average concentrations of PM 2.5 are in the top 10 percentile among all Californian census tracts as shown in figure 1(a) in the supporting information section.The PM 2.5 concentration fluctuates locally and increases significantly during wildfires [18].Both annual average PM 2.5 levels and the number of days PM 2.5 exceeds standards are important metrics for characterizing PM 2.5 .These trends for Tulare County can be found in the supporting information section in figures 2(a) and (b).Short periods of significantly high PM 2.5 significantly impact human health, where PM 2.5 induced by wildfires led to 'hundreds to thousands of premature deaths.' Although exposure-response models are available to estimate the disease burden associated with PM 2.5 levels in Tulare County, these models are generic to human populations and not calibrated to Tulare.Based on a limited understanding of interactions and our lack of knowledge about vulnerability caused by other stressors in the Tulare population, there is significant uncertainty in applying risk calculations using standard PM 2.5 exposure-response models.Instead, we must consider the results of other stressors and how they might enhance the PM 2.5 burden.

Ozone
Ozone concentrations in an area are not constant and fluctuate depending on local meteorological conditions [15].Additionally, Turner et al found that 'long-term ambient O 3 [ozone] exposure contributes to the risk of respiratory and circulatory mortality' [19].Similar to PM 2.5 , many areas of Tulare County have yearly average ozone concentrations in the top 10 percentile among Californian census tracts.In the supporting information file, figure 1(c) provides a CalEnviroScreen map of ozone indicators by census tract in the Tulare County area, and figure 2(b) shows the measured daily maximum 8 h concentrations, revealing a significant number of exceedances.This information can provide input for a risk assessment.However, what is not clear is how to combine the high ozone and high PM 2.5 levels.It is not likely that they are simply additive, and the two impacts are likely amplified by exposure to other stressors.

Drinking-water quality
Drinking water contamination disproportionately impacts rural communities in CA, primarily where water quality in domestic wells is not well regulated [11].Many rural census tracts of Tulare County have drinking water that ranks in the top 10 percentile of worst drinking water quality among Californian census tracts.Our estimated county-level rank relative to all CA census tracts is the 80th percentile.The contaminants considered are all listed on the EPA MCL and total coliform rule.In Tulare County, the following contaminants exceed MCLs in a significant proportion of municipal wells: 1,2,3, trichloropropane (Volatile Organic Compound or VOC), tetrachloroethene (VOC), nitrate (nutrient), arsenic, perchlorate, manganese (trace element), and uranium (radionuclide) [11].

Pesticides
Dairy, livestock, citrus, nuts, and grapes are the largest farm commodities in Tulare County [20].These products involve the intensive use of herbicides, insecticides, and antimicrobials.Previous literature has found strong associations between pesticide exposures and numerous health risks [14].Gunier et al connected pesticide exposure to 'significant decreases in Full-Scale IQ' [21].Tulare County has a population-weighted equivalent ranking at the 86th percentile for the CalEnviroScreen pesticide indicator relative to all CA census tracts.Figure 1(c) in the supporting information file shows the CalEnviroScreen pesticide usage indicators map.This information is not sufficient for making a risk assessment.However, it again provides a quantitative metric of stressor exposure to substances that impact immune systems and cell function to blunt defenses to other stressor exposures.

Measures of disease 3.2.1. Infant mortality and birth weight
Infant mortality is defined as medical conditions resulting in the death of an infant less than a year old [22].Moreover, infant mortality has been used as an indicator of the overall health of a population.In addition, maternal exposure to PM 2.5 during both preconception and pregnancy, is associated with increased risks of congenital malformations such as abdominal wall defects [17].Although infant mortality can be associated with infant health, it may not be the best indicator of infant health.The CalEnviroScreen indicator for birth rate shows that rates of low birth weight (2009)(2010)(2011)(2012)(2013)(2014)(2015) in some areas of Tulare County are in the top 10%-30% of CA.Studies have linked PM 2.5 to low birth weight [12].Visalia, a city in Tulare with some of the highest ozone and PM 2.5 levels statewide, is in the top 10% of the state for low birth weight.

Socioeconomic stressors
Although socioeconomic stressors are linked to health status and, in some cases, to health impacts in the current literature, there has been limited guidance on their use in disease burden studies either as single entities or as amplifying factors for environmental stressors.Here we consider the quantitative measures of this category of stressors in Tulare County and its potential interactions.

Linguistic isolation
The US Census Bureau defines linguistic isolation as households that lack at least one person, aged 14 or older, who is proficient in English [23].Zhang et al found significant associations between linguistic isolation and higher mortality among older Mexican American [24].This association is consistent with previous studies linking language barriers with poor health services and unequal access to healthcare [25].Linguistic isolation can also amplify the health impacts of environmental exposures by reducing social interactions, and employment opportunities.The Census Bureau American Community Survey (ACS) Estimates for Linguistic Isolation [23] reports that 28.8% (±1.1) of the Tulare population has limited English ability compared to 19.5% (±1.1) for the CA population.Within Tulare, many census tracts ranked above the 90th percentile for linguistic isolation in CalEnviroScreen results.The populated-weighted equivalent ranking for Tulare relative to other census tracts is at the 83rd percentile.

Poverty
Results in CalEnviroScreen show a clear association between poverty and environmental stressors.The US Census Bureau uses income thresholds based on family size to determine an individual's poverty status during the previous year [23].In addition, psychosocial and chronic stress, resulting from 'pronounced' income inequality, led to 'increased illness susceptibility' [26].Within Tulare, many census tracts ranked above the 90th percentile for poverty in CalEnviroScreen results.The populated-weighted equivalent ranking for Tulare relative to other CA census tracts is at the 81st percentile.Poverty determines the quality of neighborhoods that people inhabit and thus their exposure to environmental stressors and socio-economic stressors.Poverty also impacts access to healthy food, medical services, and community resources in ways that amplify the impact of exposure to other stressors.Thus, we consider poverty to be linked to almost every stressor.

Unemployment
We obtained unemployment data from CalEnviroScreen, which uses the US Census Bureau's ACS [23].The ACS considers individuals aged 16 and older for unemployment status.Unemployment status influences an individual's mental and physical health [27].Individuals with job instability are more likely to experience symptoms of psychological problems such as depression and anxiety than those employed.Unemployment has been associated with a decline in physical health, self-reported illnesses, cancer, cardiovascular diseases, and mortality.'According to ACS data for 2016-2020, Tulare County, at 9.9%, has one of the highest unemployment rates among counties in CA compared to statewide, at 6.2% [23].

Walkability
We obtained spatial and quantitative walkability data from the EPA National Walkability Index [28].Walkable neighborhoods and parks have been shown to be influential on human health [29].A review conducted by Wolch et al found 'major health benefits' from 'physical activity,' However, not all communities have equitable access to walkable neighborhoods [13].Dunton et al found that children's use of neighborhood parks substantially increased when parks were closer and had more greenery [30].Wolch et al found significant associations between body mass index (BMI)-a measurement sometimes used for obesity-and poor access to local parks [13].BMI has also been associated with several health risks, including a 'variety of cardiovascular risk factors' and overall 'greater cardiovascular risk' in observational studies.
According to the EPA's National Walkability Index, most areas in Tulare are in the 'Least Walkable' category.As noted in the method section, poor walkability access is linked to cardiovascular disease.Other prominent environmental stressors in Tulare linked to cardiovascular disease are PM 2.5 , ozone, and drinking water quality.

Diet
The availability of healthy, affordable foods has been linked to better diets and reductions in chronic diseases [31].Socioeconomically vulnerable communities face obstacles to accessing healthier food options [22].In 2014, 29% of the population in Tulare County lived in poverty, and 29% of low-income households were food insecure.A study conducted by Smed et al found that unemployment led to increases in 'consumption of saturated fat, total fat and protein due to increased consumption of animal-based foods' [32].The US Department of Agriculture University of Wisconsin Population Health Institute food-environment index considers food accessibility and insecurity [33].The index scores Tulare County as 7.0/10.0,scoring worse than the CA average of 8.8/10.0.The obesity rate in 2018 for Tulare is 36.3%,compared to the CA average of 25.3%.Lack of access to food and food insecurity have both direct impacts on health and the potential to amplify health impacts of environmental exposures.

Mortality data
As a final indicator reflecting cumulative stressor exposures and health outcomes, we reviewed mortality data for Tulare compared to state levels.Table 2 summarizes the leading causes of death in Tulare compared to CA as obtained from the CDC WONDER [22].The crude rates per 100 000 for Alzheimer's disease, diseases of the circulatory system, COVID-19, external causes of mortality, and influenza and pneumonia are higher for Tulare than in the CA.The crude rates-per 100 000 people-for chronic lower respiratory diseases, neoplasms, cerebrovascular diseases, diabetes mellitus, and diseases of the genitourinary system are lower in Tulare relative to CA.It is of interest that the rate of diabetes mellitus is lower despite the high endocrine, nutritional, and metabolic diseases rate.

Discussion
We have reviewed and evaluated both the magnitude and potential interactions among environmental and non-environmental (primarily socioeconomic) stressors in Tulare County.We have also cited data showing that Tulare County residents experience a greater disease burden than CA overall.Our evaluation reveals that the population living within the census tracts of Tulare County experiences environmental stressors at significantly higher levels relative to many other Californian census tracts, specifically PM 2.5 , ozone, and drinking water quality.A population's vulnerability to these environmental stressors is based upon numerous endogenous and exogenous factors [1].Endogenous factors are defined as factors inherent to an individual, such as age, gender, genetics, and pre-existing disease.In contrast, exogenous factors are defined as external stressors such as pollution.Due to these factors, a population will exhibit a range of vulnerabilities to environmental stressors that will determine their specific response.
The vulnerability of a population due to endogenous factors can be represented at the cellular level through population statistics-i.e.average, median, etc of the individuals' telomere length.The telomere length is a unified metric for bioaging and is an absolute measure of an individual's biological age, independent of chronological time [34].When genotoxic stressors, such as PM 2.5 , ozone, etc, cause damage at the cellular level, the damaged cells must be replaced through cell division [35].However, with each cell division, 'a small part' of the telomere is not copied; thus, the telomere length is reduced over time.When the length of the telomere reaches its 'critical length,' it allows for 'DNA losses,' and the body is subsequently more 'prone to express disease.'Specifically, the 'DNA damage to telomere activities signals alters the sirtuin1 sledding to mitochondrial dysfunction.'Mitochondria are essential to the cell's regular function, and cells with dysfunctioning mitochondria are less resilient to oxidative stress [8].Each person is born with a finite number of cell divisions available.Thus, a statistical representation of a population's vulnerability can be likened to how close the telomere length is to the critical length.A study linking the telomere length to PM 2.5 and cardiovascular disease associated shorter length with a higher risk of cardiovascular cancer [36].Furthermore, Wong et al found that findings included a decrease in 'relative telomere length of −0.04 units' for each additional 'milligram per cubic meter per hour increase in cumulative PM 2.5 exposure in the prior month.'Furthermore, the apparent mechanism is that PM 2.5 can induce the generation of reactive oxygen species and oxidative stress.Ozone also induces the generation of reactive oxygen species [35].At the cellular level, these two stressors mechanically induce cell damage and cell division.The same can be concluded from studies investigating socioeconomic stress and telomere length [37].Therefore, multiple stressors at the cellular level cause overlapping influences.
Due to these endogenous factors, a population will exhibit a range of vulnerability to environmental stressors that will determine their specific response.Chi et al demonstrated how the association between air pollution and cardiovascular diseases is impacted by neighborhood social economic status [38].Individual and neighborhood socioeconomic status and the association between air pollution and cardiovascular diseases provide a useful example of how a population's response to one stressor is amplified by a second stressor.Figure 1 shows two exposure-response curves, which are based on the increasing risk reported by Chi et al.The lower curve is based on risk for all study participants (13% per increase of 5 ug m −3 PM 2.5 continuous exposure) versus the upper curve for participants in the lowest NESES (39% increases per 5 ug m −3 PM 2.5 continuous exposure).We obtain these curves using the increase of relative risk and the assumption that the baseline cardiovascular risk for this population is 0.07.
In a standard risk assessment, the population response to a stressor, such as PM 2.5 , is modeled with an exposure-response curve.We illustrate how increasing vulnerability can impact population responses to a range of exposures in figures 2 and 3.These figures all have log-normal scales with upper curves (a) providing theoretical exposure-response curves and the lower curves (b) showing the theoretical distribution of exposures within the population.The upper (blue curve) in figure 2(a) shows, for a baseline case, exposure to a single stressor and the fraction of the population likely responding above a given exposure on the x-axis.The lower (black) curve, in figure 2(b), shows the baseline exposure distribution for a single stressor, the fraction of the population exposed above x.
The benchmark exposure in figure 2(a), blue colored curve, represents the departure of acceptable exposures to those resulting in a measurable response.The baseline curve in figure 2(a) has a benchmark exposure at EE10, meaning the effective exposure that will result in a 10% population response.The cumulative exposure curve in figure 1(b) illustrates the cumulative distribution of exposure.The one-sided 95% upper confidence level (UCL) represents the upper margin of protection required to protect at least 95% of the population from an exposure-response.The margin of exposure is the ratio of the corresponding exposure at the UCL to the EE10.
We next consider how the curves in figure 2 change when additional stressors increase the vulnerability of a population, exacerbate pre-existing stressors, or both.Socioeconomic stressors such as poverty can increase a population's vulnerability to environmental stressors such as PM 2.5 and ozone.A secondary stressor's influence on the susceptibility of a population can be modeled by transforming the baseline stressor exposure-response curve.For example, a greater proportion of the population will respond to a given exposure.In figure 2, the baseline curve (blue) translates to the left, increasing the overlap between the exposure and exposure-response curves.This greater overlap reflects an increased vulnerability of a population and a reduced margin of exposure.Although it is not illustrated here, some stressors (i.e.poverty) can increase the overall magnitude of the exposure distribution relative to the baseline exposure, further decreasing the margin of exposure.
In addition to translating the baseline curve, exposures to multiple stressors (one or more additional stressors; two or more total stressors) can cause a more complex transformation of the baseline curve, as shown in figure 3. Here, the baseline curve (illustrated by the blue curve) transforms into a 'flatter' new curve (illustrated by the red curve).Here additional stressors increase the vulnerability by adding greater variance in population response.This overlap represents a more significant fraction of the population exposed to the stressor.
Based on the examples of potential stressor-stressor interactions in the results section, we compiled a list of the potential relevant interactions for Tulare County.In figure 4, we illustrate all the associative links between environmental and socioeconomic stressors discussed in this study and their contribution to the disease burden.The density of the linking lines reflects preliminary estimates of the likely magnitude of the link.It remains for future efforts to provide a more quantitative assessment of these links.

Conclusion
The population of Tulare County is exposed daily to environmental stressors from PM 2.5 , ozone, pesticides, and poor drinking water quality.Their vulnerability to these environmental stressors is further exacerbated by socioeconomic stressors, including poverty, diet, walkability, linguistic isolation, and unemployment.Tulare County experiences a more significant disease burden than CA state averages.We have proposed the use of altered exposure-response curves to characterize the increasing disease burden that arises from the vulnerability imposed by exposure to multiple stressors.
The current practice in health impact studies linking individual environmental stressors and their respective measures of diseases has guided the development of policies and treatments for managing individual stressors.However, many communities continue to be exposed daily to numerous stressors that may individually meet regulatory standards but might significantly magnify risk due to the synergistic effect.This study is limited by the quality of the data available.Limits on how to actually allocate this at a higher level.We are lacking geographic resolution and temporal resolution for a longitudinal study.Until these issues can be addressed, there is still high level of uncertainty.
This study introduces theoretical methods to model a vulnerability and effective exposure to multiple stressors.These methods provide the potential to quantify the synergistic risks from multiple stressors on specific measures of disease through computational models.Quantifying these more complex relationships can better guide current measurements and future forecasting for stressors amplified by climate change.These potential models may optimize the reduction of specific stressors to lessen the synergistic impact to reshape the future of policies and treatments to mitigate the disease burden of historically marginalized communities.
All data that support the findings of this study are included within the article (and any supplementary information files).

Figure 1 .
Figure 1.The shift in vulnerability in exposure-response trends due to changes in socioeconomic score.The red curve represents the neighborhood with the lowest socioeconomic score and the black curve represents all neighborhoods in the study.

Figure 2 .
Figure 2. Shifted exposure-response for a risk assessment of a population due to exposure to multiple stressors (one or more stressors).(a) The baseline vulnerability of the population is represented by the blue curve with benchmark effective exposure 10 (EE10).Coexposure increases the baseline vulnerability of the population by translating it to the left.This heightened vulnerability of a population (illustrated by the red curve) results in a smaller margin of exposure.(b) The baseline exposure distribution for a single stressor.With the same exposure distribution, there is less protection and this could be even lower if a second stressor increases the overall magnitude of the exposure distribution [39].

Figure 3 .
Figure 3. Varied exposure-response for a risk assessment of a population due to exposure to multiple stressors.(a) The baseline population vulnerability (blue curve) 'flattens' , resulting in a more variant response.(b) The increase in variance can further decrease the margin of exposure [39].

Figure 4 .
Figure 4.A visual illustrating all connections between environmental and socioeconomic stressors is discussed in the above sections.

Table 1 .
For the stressors listed with the asterisk ( * ), we have taken the data from CalEnviroScreen, which uses and interprets data from the original sources cited in the table.

Table 2 .
Leading causes of death in Tulare compared with California (the asterisk * next to a disease indicates the disease has higher mortality in Tulare[22].Reproduced from[22].CC BY 4.0).